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Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing

Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, Kang Liu


Abstract
Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases. Previous work has observed that leveraging lexico-logical alignments is very helpful to improve parsing performance. However, current attention-based approaches can only model such alignments at the token level and have unsatisfactory generalization capability. In this paper, we propose a new approach to leveraging explicit lexico-logical alignments. It first identifies possible phrase-level alignments and injects them as additional contexts to guide the parsing procedure. Experimental results on Squall show that our approach can make better use of such alignments and obtains an absolute improvement of 3.4% compared with the current state-of-the-art.
Anthology ID:
2022.acl-short.31
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
283–289
Language:
URL:
https://aclanthology.org/2022.acl-short.31
DOI:
10.18653/v1/2022.acl-short.31
Bibkey:
Cite (ACL):
Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, and Kang Liu. 2022. Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 283–289, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (Sun et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-short.31.pdf
Software:
 2022.acl-short.31.software.zip